{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorflow'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[0;32m      3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpreprocessing\u001b[39;00m \u001b[39mimport\u001b[39;00m MinMaxScaler\n\u001b[1;32m----> 4\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtensorflow\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mtf\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpandas\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpd\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"data.npy\"\n",
    "\n",
    "with open(file_path, \"rb\") as file:\n",
    "    data_array = np.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2400, 45)\n"
     ]
    }
   ],
   "source": [
    "# Assuming you already have the 'data_array' with shape (24, num_columns)\n",
    "num_copies = 100\n",
    "\n",
    "# Create an array of shape (num_copies, 1) to repeat 'data_array' vertically\n",
    "repeated_array = np.tile(data_array, (num_copies, 1))\n",
    "\n",
    "# Stack the repeated arrays vertically\n",
    "data_array_extended = np.vstack(repeated_array)\n",
    "\n",
    "print(data_array_extended.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2400"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_array_extended)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shift_data(data_array, window_size=5):\n",
    "  X = []\n",
    "  y = []\n",
    "  for i in range(len(data_array_extended)-window_size):\n",
    "    row = [r for r in data_array_extended[i:i+window_size]]\n",
    "    X.append(row)\n",
    "    label = [data_array_extended[i+window_size]]\n",
    "    y.append(label)\n",
    "  return np.array(X), np.array(y)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2395, 5, 45), (2395, 1, 45))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X,y = shift_data(data_array=data_array_extended)\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scalers_x = {}\n",
    "for i in range(X.shape[2]):\n",
    "    scalers_x[i] = MinMaxScaler().fit(X[:, :, i])\n",
    "\n",
    "scalers_y = {}\n",
    "for i in range(y.shape[2]):\n",
    "    scalers_y[i] = MinMaxScaler()\n",
    "    scalers_y[i] =  MinMaxScaler().fit(y[:, :, i]) \n",
    " \n",
    "for i in range(X.shape[2]):\n",
    "    X[:, :, i] = scalers_x[i].transform(X[:, :, i])\n",
    "\n",
    "for i in range(y.shape[2]):\n",
    "    y[:, :, i] = scalers_y[i].transform(y[:, :, i]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2395, 5, 45)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1800, 5, 45),\n",
       " (1800, 1, 45),\n",
       " (300, 5, 45),\n",
       " (300, 1, 45),\n",
       " (295, 5, 45),\n",
       " (295, 1, 45))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X3_train, y3_train = X[:1800], y[:1800]\n",
    "X3_val, y3_val = X[1800:2100], y[1800:2100]\n",
    "X3_test, y3_test = X[2100:], y[2100:]\n",
    "X3_train.shape, y3_train.shape, X3_val.shape, y3_val.shape, X3_test.shape, y3_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(X.shape[2]):\n",
    "#     X[:, :, i] = scalers_x[i].inverse_transform(X[:, :, i])\n",
    "\n",
    "# for i in range(y.shape[2]):\n",
    "#     y[:, :, i] = scalers_y[i].inverse_transform(y[:, :, i]) \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.losses import MeanSquaredError\n",
    "from tensorflow.keras.metrics import RootMeanSquaredError\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 64)                28160     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 18)                1170      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 45)                855       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 30,185\n",
      "Trainable params: 30,185\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model5 = Sequential()\n",
    "model5.add(InputLayer((5, 45)))\n",
    "model5.add(LSTM(64))\n",
    "model5.add(Dense(18, 'relu'))\n",
    "model5.add(Dense(45, 'linear'))\n",
    "\n",
    "model5.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp5 = ModelCheckpoint('model5/', save_best_only=True)\n",
    "model5.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "56/57 [============================>.] - ETA: 0s - loss: 0.2157 - root_mean_squared_error: 0.4644"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57/57 [==============================] - 22s 233ms/step - loss: 0.2157 - root_mean_squared_error: 0.4644 - val_loss: 0.1997 - val_root_mean_squared_error: 0.4471\n",
      "Epoch 2/10\n",
      "51/57 [=========================>....] - ETA: 0s - loss: 0.1916 - root_mean_squared_error: 0.4378"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57/57 [==============================] - 10s 173ms/step - loss: 0.1910 - root_mean_squared_error: 0.4371 - val_loss: 0.1820 - val_root_mean_squared_error: 0.4267\n",
      "Epoch 3/10\n",
      "57/57 [==============================] - ETA: 0s - loss: 0.1747 - root_mean_squared_error: 0.4181"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57/57 [==============================] - 9s 153ms/step - loss: 0.1747 - root_mean_squared_error: 0.4181 - val_loss: 0.1669 - val_root_mean_squared_error: 0.4086\n",
      "Epoch 4/10\n",
      "50/57 [=========================>....] - ETA: 0s - loss: 0.1609 - root_mean_squared_error: 0.4012"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57/57 [==============================] - 6s 113ms/step - loss: 0.1602 - root_mean_squared_error: 0.4003 - val_loss: 0.1531 - val_root_mean_squared_error: 0.3912\n",
      "Epoch 5/10\n",
      "52/57 [==========================>...] - ETA: 0s - loss: 0.1474 - root_mean_squared_error: 0.3840"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57/57 [==============================] - 6s 106ms/step - loss: 0.1468 - root_mean_squared_error: 0.3832 - val_loss: 0.1403 - val_root_mean_squared_error: 0.3744\n",
      "Epoch 6/10\n",
      "53/57 [==========================>...] - ETA: 0s - loss: 0.1349 - root_mean_squared_error: 0.3673"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57/57 [==============================] - 6s 112ms/step - loss: 0.1345 - root_mean_squared_error: 0.3668 - val_loss: 0.1286 - val_root_mean_squared_error: 0.3586\n",
      "Epoch 7/10\n",
      "51/57 [=========================>....] - ETA: 0s - loss: 0.1238 - root_mean_squared_error: 0.3518"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57/57 [==============================] - 6s 103ms/step - loss: 0.1234 - root_mean_squared_error: 0.3513 - val_loss: 0.1182 - val_root_mean_squared_error: 0.3437\n",
      "Epoch 8/10\n",
      "50/57 [=========================>....] - ETA: 0s - loss: 0.1139 - root_mean_squared_error: 0.3376"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57/57 [==============================] - 7s 117ms/step - loss: 0.1135 - root_mean_squared_error: 0.3369 - val_loss: 0.1089 - val_root_mean_squared_error: 0.3299\n",
      "Epoch 9/10\n",
      "50/57 [=========================>....] - ETA: 0s - loss: 0.1053 - root_mean_squared_error: 0.3245"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57/57 [==============================] - 10s 176ms/step - loss: 0.1047 - root_mean_squared_error: 0.3237 - val_loss: 0.1007 - val_root_mean_squared_error: 0.3172\n",
      "Epoch 10/10\n",
      "55/57 [===========================>..] - ETA: 0s - loss: 0.0970 - root_mean_squared_error: 0.3114"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _update_step_xla, lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: model5\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57/57 [==============================] - 11s 189ms/step - loss: 0.0970 - root_mean_squared_error: 0.3115 - val_loss: 0.0935 - val_root_mean_squared_error: 0.3056\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x232e1c30d90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model5.fit(X3_train, y3_train, validation_data=(X3_val, y3_val), epochs=10, callbacks=[cp5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_predictions2(model, X, y, start=0, end=100):\n",
    "    predictions = model.predict(X)\n",
    "    p_preds, temp_preds = predictions[:, 0], predictions[:, 1]\n",
    "    p_actuals, temp_actuals = y[:, 0 ,0], y[:, 0 ,1]\n",
    "    df = pd.DataFrame(data={'Temperature Predictions': temp_preds,\n",
    "                            'Temperature Actuals':temp_actuals,\n",
    "                            'Pressure Predictions': p_preds,\n",
    "                            'Pressure Actuals': p_actuals\n",
    "                            })\n",
    "    plt.plot(df['Temperature Predictions'][start:end])\n",
    "    plt.plot(df['Temperature Actuals'][start:end])\n",
    "    plt.plot(df['Pressure Predictions'][start:end])\n",
    "    plt.plot(df['Pressure Actuals'][start:end])\n",
    "    return df[start:end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(295, 1, 45)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y3_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 1s 3ms/step\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature Predictions</th>\n",
       "      <th>Temperature Actuals</th>\n",
       "      <th>Pressure Predictions</th>\n",
       "      <th>Pressure Actuals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.382942</td>\n",
       "      <td>0.143779</td>\n",
       "      <td>0.294220</td>\n",
       "      <td>0.203434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.373328</td>\n",
       "      <td>0.015269</td>\n",
       "      <td>0.279453</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.381536</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.291119</td>\n",
       "      <td>0.018277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.367479</td>\n",
       "      <td>0.031893</td>\n",
       "      <td>0.285993</td>\n",
       "      <td>0.044930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.366263</td>\n",
       "      <td>0.108934</td>\n",
       "      <td>0.290771</td>\n",
       "      <td>0.071282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.379920</td>\n",
       "      <td>0.146255</td>\n",
       "      <td>0.269114</td>\n",
       "      <td>0.205999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.382942</td>\n",
       "      <td>0.143779</td>\n",
       "      <td>0.294220</td>\n",
       "      <td>0.203434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.373328</td>\n",
       "      <td>0.015269</td>\n",
       "      <td>0.279453</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.381536</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.291119</td>\n",
       "      <td>0.018277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.367479</td>\n",
       "      <td>0.031893</td>\n",
       "      <td>0.285993</td>\n",
       "      <td>0.044930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Temperature Predictions  Temperature Actuals  Pressure Predictions  \\\n",
       "0                  0.382942             0.143779              0.294220   \n",
       "1                  0.373328             0.015269              0.279453   \n",
       "2                  0.381536             0.000000              0.291119   \n",
       "3                  0.367479             0.031893              0.285993   \n",
       "4                  0.366263             0.108934              0.290771   \n",
       "..                      ...                  ...                   ...   \n",
       "95                 0.379920             0.146255              0.269114   \n",
       "96                 0.382942             0.143779              0.294220   \n",
       "97                 0.373328             0.015269              0.279453   \n",
       "98                 0.381536             0.000000              0.291119   \n",
       "99                 0.367479             0.031893              0.285993   \n",
       "\n",
       "    Pressure Actuals  \n",
       "0           0.203434  \n",
       "1           0.000000  \n",
       "2           0.018277  \n",
       "3           0.044930  \n",
       "4           0.071282  \n",
       "..               ...  \n",
       "95          0.205999  \n",
       "96          0.203434  \n",
       "97          0.000000  \n",
       "98          0.018277  \n",
       "99          0.044930  \n",
       "\n",
       "[100 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions2(model5, X3_test, y3_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
